By Lonnie Shekhtman
Wearable medical sensors used widely in hospitals and clinics are spreading into the mainstream as tech companies increasingly incorporate them into popular electronics, from Apple’s smart watches to Fitbit fitness bands.
Princeton engineers are working to take these sensor technologies one step further by developing software that could one day use multiple health clues from wearable sensors to diagnose myriad diseases in real-time. When fully developed, the system would warn a patient who is developing diabetes, for example.
In a paper in the journal IEEE Transactions on Multi-Scale Computing Systems, researchers led by Niraj Jha reported that their system, the Hierarchical Health Decision Support System (HDSS), used biomedical data to successfully detect five diseases in simulations created from an amalgamation of patient data. The paper, published in the journal’s Oct.-Dec. issue, states that the system diagnosed type-2 diabetes with 78 percent accuracy, arrhythmia with 86 percent accuracy, urinary bladder disorder with 99 percent accuracy, hypothyroid with 95 percent accuracy and renal pelvis nephritis with 94 percent accuracy.
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